Quantum Machine Learning is a growing field with the potential to unlock several opportunities in groundwater management. A novel approach integrating advanced quantum machine learning (QML) models with classical machine learning (ML) algorithms for robust groundwater quality assessment, using the SABHQ (Systematically Averaged Battery Hybrid Quantum) method, is proposed. We attempted to reduce noise and enhance generalization by stimulating composite sampling across both ML and QML pipelines. Accuracy, precision, recall, and F1 scores were calculated across all models, using normalization and aggregation strategies to achieve relatively unbiased benchmarking. We observed that the dominant predictors, such as EC, Cl, HCO3, SO4, Na, TH, Latitude, and Longitude, reflect both spatial variability and anthropogenic influences on groundwater quality. The systematic averaging method yielded noticeable improvements for QML models, with QCNN achieving accuracy and F1 scores of up to 0.95 and 0.98, respectively, outperforming classical machine learning models. We report that QCNN metrics improved due to aggregation, noise reduction, and possibly quantum entanglement effects, leading to better generalization, whereas VQC showed gradual but minor improvements. We believe our work can be extended to other environmental domains, not only to refine the method we proposed but also to enhance its adaptability.
Jagadish Kumar Mogaraju (Mon,) studied this question.